Things to know for Data Science Certification

Data Science related certifications are booming as the related job market is heating up. It is important to understand the basics of Data Science before signing up for a Data Science certification course, so you can make an informed decision regarding your Data Science Certification

Concepts of Statistics

Statistics is a structured way of collecting, analyzing, interpreting and presenting data. This finally gets transformed into a visual representation. Statistics got most of its knowledge from Mathematics and helped mostly in coming out with insights and inferences. The concepts of Statistics are nothing new, they have been around since the 5th century B.C. It has now evolved into many forms and more and more applications of Statistics are coming up in various domains, particularly finance, where heavy quantitative analysis is coming out and models are getting more sophisticated for risk analytics, for picking the right investment strategies and so on. There are two forms of statistics: There is one segment that is more interested in pure statistics and there is one segment that understands and goes into Applied Statistics. Applied Statistics is the concepts or subjects which are more suitable for practical applications, mostly in the realm of economics. A lot of people are also into quantitative analysis.

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Power of visualization

Once you have the statistics derived from the data, now comes the time when you want to put these ideas in some simple looking plots and visuals. Otherwise, it so happens that the statistical ideas are so complex to understand that it is not always easy for us to put a formula on a slide and then go to the head of the business saying, “this is the statistics I have found from my data”. It is not really a good way of communicating your findings. A better way of comprehending your findings is to put them in a good looking chart which is very precise, showing only the relevant details and hiding all the complex or sophisticated looking formulae or raw data. A good visual will also reveal some hidden patterns which may be hard to see from raw data. The scope of visual analytics is not only limited to putting some numbers on a slide and just say that my business is doing very good or very bad, but it translates into things like finding out interactions between the various attributes, finding out the pattern in a Geo spatial sort of representation. So for example, if I want to understand my product purchases coming from various regions across the country or the world, I can put such sort of data into a geospatial chart or something like a map. I could then try to come out with a heap map kind of a visualization that tells me if there is a heavy concentration of my customer base in Asia Pacific Region, then I know that my stronghold is in this area. Lot more tools are coming up in the areas of knowledge representation to add to the arsenal.

Resources

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